The urinary RNA atlas of patients with chronic kidney disease

Chronic kidney disease (CKD) represents a significant global health burden. Currently employed CKD biomarkers are influenced by various factors and lack accuracy in reflecting early-stage renal fibrosis severity. Consequently, there is an urgent need for the identification of early, noninvasive CKD biomarkers. Urine, easily collectible and kidney-derived, has demonstrated potential as a diagnostic source for various kidney diseases by leveraging its RNA content. To address this, we obtained RNA-seq data pertaining to urinary RNAs from both CKD patients and healthy controls via the Gene Expression Omnibus database (GEO). The DEseq2 software was utilized to identify differentially expressed RNAs (DE-RNAs). To evaluate the overall accuracy of these DE-RNAs in urine, we performed Receiver Operating Characteristic analysis (ROC). Selected urinary RNAs were subsequently validated using reverse-transcription quantitative real-time Polymerase Chain Reaction (qRT-PCR) in conjunction with ROC analysis. Computational and experimental analyses revealed significant increases in miR-542-5p, miR-33b-5p, miR-190a-3p, miR-507, and CSAG4 within the urine of CKD patients, exhibiting high AUC values. In conclusion, our findings suggest that urinary RNAs hold promise as diagnostic biomarkers for CKD.


Identification of dysregulated RNAs (DE-RNAs)
To mitigate batch effects and unwanted variations in RNA counts within each sample of the two GEO datasets, the SVA package was employed.Subsequently, the DEseq2 software was utilized to identify differentially expressed RNAs (DE-RNAs).In our analysis, RNAs meeting the following criteria were considered differentially expressed: reads per million greater than 1, p-value less than 0.01, and the absolute value of log2 fold change (FC) higher than 1.

Collection of urine samples and RNA isolation
Twenty biopsy-proven CKD patients and twenty healthy volunteers were recruited from Jurong People Hospital, and their whole stream early morning urine samples were collected.The demographic and clinical characteristics of the CKD patients and healthy volunteers are presented in Table 1.Healthy controls were defined as individuals without abnormalities identified through routine urinalysis and having normal renal function [estimated glomerular filtration rate (eGFR) > 90 ml/min/1.73m2].Subsequently, the urine samples were centrifuged at 3000 g for 30 min at 4 °C, and the supernatant was carefully collected and stored at − 80 °C.To extract RNA from the supernatant, the TRIzol Reagent (Ambion, Life Technologies) was employed following the manufacturer's protocol (Ambion, Life Technologies, USA).Furthermore, the concentration and purity of the extracted RNA were assessed using the absorbance ratio at 260/280 measured with a NanoDrop 2000 spectrophotometer (Thermo, USA).

Ethics approval
This study was approved by the Ethics Committee of Jurong People Hospital.Informed consent was waived by the Ethics Committee of Jurong People Hospital.All the experiments were performed in accordance with the approved guidelines and complied with the Declaration of Helsinki.The urine samples were collected from the residual urine after routine urine test, which belongs to the exemption of informed consent.Therefore, the participants didn't provide written informed consent.

Differential abundance analysis of urinary RNAs in CKD patients compared to healthy controls
To explore the abundance of urinary RNAs, we performed an analysis using a dataset consisting of 80 CKD samples (GSE121978) and 47 healthy controls (GSE128359).This analysis encompassed a wide range of RNA types, including 36 circRNAs, 58,427 long RNAs (comprising mRNAs and lncRNAs), 1,636 miRNAs, 531 piRNAs, and 24 tRNAs (Table S1).Notably, the application of tSNE analysis revealed clear clustering of the CKD and healthy control samples based on their urinary RNA profiles (Fig. 1A).Furthermore, through visual examination of the volcano plot and heat map, distinct patterns of urinary RNA expression were observed, effectively differentiating CKD patients from healthy controls (Fig. 1B,C).
Using the DESeq2 software, we identified a total of 138 differentially expressed RNAs (DE-RNAs) meeting the significance criteria of |log2 fold change (FC)| ≥ 1 and a p-value ≤ 0.01.Out of these, 16 RNAs were found to be upregulated, while 122 were downregulated in CKD samples compared to healthy controls (Table S2).

Selection of candidate urinary RNAs for the diagnosis of CKD
To assess the overall accuracy of 138 differentially expressed RNAs (DE-RNAs) in discriminating between various groups of CKD patients and healthy controls, ROC analysis was conducted.Specifically, these DE-RNAs were selected from a pool of urinary RNAs with reads above 1 per million.The analysis revealed that among the 100 RNAs under investigation, each exhibited distinct area under the curve (AUC) values, with the highest AUC (AUC = 0.85625) observed for hsa-miR-542-5p (Table S3).

Discussion
Until now, the diagnosis of CKD has primarily relied on urinary protein analysis and assessing changes in the glomerular filtration rate.Diagnostic biomarkers at the gene transcription level, widely employed in the diagnosis of tumors and various diseases, have not been extensively utilized for CKD due to the invasive nature of kidney biopsy.However, as growing evidence indicates the presence of RNAs in urine, similar to serum/plasma samples,  the analysis of urinary RNAs presents a new avenue to gain insights into CKD.This non-invasive approach offers a promising opportunity to explore and understand CKD in a novel way.
In this study, high-throughput RNA-sequencing was employed to analyze urinary RNAs with the aim of identifying potential biomarkers for CKD diagnosis.Urine, being easily collectible, has been regarded as an ideal source for kidney-related disease biomarkers.Our analysis revealed the presence of diverse RNA classes in  www.nature.com/scientificreports/urine, including mRNA, lncRNA, circRNA, miRNA, piRNA, and tRNA.Notably, miR-542-5p, miR-33b-5p, miR-190a-3p, miR-507, and CSAG4 in urine emerged as promising biomarkers for CKD.Both plasmatic transrenal and postrenal cell-free RNA were identified in urine; however, the focus of existing studies has predominantly been on RNA released into urine postrenally through mechanisms such as apoptosis, necrosis, and active secretion.Previous investigations have successfully detected survirin, cytokeratin 20, mucin 7, and Ki-67 mRNAs in the urine of patients with bladder cancer and various urinary tract infections, highlighting the cells of the urogenital tract as major contributors of urinary RNAs 12 .Functional analysis of the identified miR-542-5p, miR-33b-5p, miR-190a-3p, miR-507, and CSAG4 biomarkers revealed their involvement in signal pathways relevant to renal damage, such as TNF receptor signaling pathway and p38 MAPK signaling pathway (Fig. 7).These findings suggest that miR-542-5p, miR-33b-5p, miR-190a-3p, miR-507, and CSAG4 might be actively or passively released by the kidneys during the progression of CKD.However, further research is necessary to elucidate the exact release mechanisms of these five indicators by the kidneys.The detection and identification of RNAs in urine can be accomplished using various techniques such as RNA sequencing, microarray technologies, or reverse-transcription quantitative real-time PCR (qRT-PCR).In our study, we utilized RNA sequencing to identify dysregulated RNAs in the urine of CKD patients.However, for practical clinical application, RNA sequencing poses challenges when applied to urine samples.Currently,

Table 1 .
The clinical profile of CKD patients and healthy volunteers.

Table 3 .
The risk score of the combination of the six urinary RNAs.